class_docstr: A simple 2D point environment.
  
  Args:
      goal (np.ndarray): A 2D array representing the goal position
      arena_size (float): The size of arena where the point is constrained
          within (-arena_size, arena_size) in each dimension
      done_bonus (float): A numerical bonus added to the reward
          once the point as reached the goal
      never_done (bool): Never send a `done` signal, even if the
          agent achieves the goal
      max_episode_length (int): The maximum steps allowed for an episode.
class_docstr_tok: ["A","simple","2D","point","environment",".","Args",":","goal","(","np.ndarray",")",":","A","2D","array","representing","the","goal","position","arena_size","(","float",")",":","The","size","of","arena","where","the","point","is","constrained","within","(","-arena_size",",","arena_size",")","in","each","dimension","done_bonus","(","float",")",":","A","numerical","bonus","added","to","the","reward","once","the","point","as","reached","the","goal","never_done","(","bool",")",":","Never","send","a","`","done","`","signal",",","even","if","the","agent","achieves","the","goal","max_episode_length","(","int",")",":","The","maximum","steps","allowed","for","an","episode","."]
docstr: list: A list of string representing the supported render modes.
docstr_tok: ["list",":","A","list","of","string","representing","the","supported","render","modes","."]
code: def render_modes(self):
      """list: A list of string representing the supported render modes."""
      return [
          "ascii",
      ]
  
file_path: garage/src/garage/envs/point_env.py
imports: import math
  import akro
  import numpy
  from garage import Environment, EnvSpec, EnvStep, StepType
label_desc: Use image node_id 5 for calling the PointEnv obj's underlying member method code with example usage: obj.render_modes() and returns: list